A mating strategy for multi-parent genetic algorithms by integrating tabu search

Multiparent crossovers have been validated their outperformance on several optimization problems. However, there are two issues to be considered - the number of parents and the disruptiveness caused by multiple parents. We present a tabu multiparent genetic algorithm (TMPGA) to address these two issues by integrating tabu search into the mating of multiparent genetic algorithms. TMPGA utilizes the tabu restriction and the aspiration criterion to sift selected parents in consideration of population diversity and selection pressure. Furthermore, the resulting mating validity further adjusts the number of parents participating in a mating. Experiments are conducted with four common test functions. The results indicate that TMPGA can achieve better performance than both two-parent GA and multiparent GA with the diagonal crossover.